{"paper":{"title":"Estimation Efficiency Under Privacy Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","math.IT","math.ST","stat.TH"],"primary_cat":"cs.IT","authors_text":"Fady Alajaji, Mario Diaz, Shahab Asoodeh, Tamas Linder","submitted_at":"2017-07-08T07:46:46Z","abstract_excerpt":"We investigate the problem of estimating a random variable $Y\\in \\mathcal{Y}$ under a privacy constraint dictated by another random variable $X\\in \\mathcal{X}$, where estimation efficiency and privacy are assessed in terms of two different loss functions. In the discrete case, we use the Hamming loss function and express the corresponding utility-privacy tradeoff in terms of the privacy-constrained guessing probability $h(P_{XY}, \\epsilon)$, the maximum probability $\\mathsf{P}_\\mathsf{c}(Y|Z)$ of correctly guessing $Y$ given an auxiliary random variable $Z\\in \\mathcal{Z}$, where the maximizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.02409","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}